code: def main():
      # Create experiment
      experiment.create(name="glu_variants")
      # Create configs
      configs = Configs()
      # Load configurations
      experiment.configs(dataclasses.asdict(configs))
  
      # Create trainer
      trainer = Trainer(configs)
      # Set models for training and loading
      experiment.add_pytorch_models({"model": trainer.model})
  
      # Start the experiment
      with experiment.start():
          # Train the model
          trainer.train()
  
imports: import dataclasses
  import torch
  from labml_helpers.module import Module
  from torch import nn
  from torch.utils.data import Dataset, DataLoader
  from labml import experiment, lab, tracker, monit, logger
  from labml.logger import Text
  from labml.utils.download import download_file
  from labml_nn.experiments.nlp_autoregression import transpose_batch
  from labml_nn.optimizers.noam import Noam
  from labml_nn.transformers import Encoder, MultiHeadAttention
  from labml_nn.transformers.feed_forward import FeedForward
  from labml_nn.transformers.models import EmbeddingsWithPositionalEncoding, TransformerLayer
  from labml_nn.transformers.utils import subsequent_mask
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types